https://github.com/singhxtushar/nextwordai

https://github.com/singhxtushar/nextwordai

Last synced: 8 months ago
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• Host: GitHub
• URL: https://github.com/singhxtushar/nextwordai
• Owner: SINGHxTUSHAR
• License: mit
• Created: 2025-01-30T05:26:06.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-01-30T06:08:31.000Z (8 months ago)
• Last Synced: 2025-01-30T06:24:52.053Z (8 months ago)
• Language: Jupyter Notebook
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# NextWordAI

![Preview Image](https://github.com/SINGHxTUSHAR/NextWordAI/blob/3b63521de3d8cfc863b6631786d8b22c734f4ba8/preview.png)

#### Project Overview:

This project focuses on developing a deep learning model for predicting the next word in a given sequence of words using Long Short-Term Memory (LSTM) networks. LSTMs are particularly effective for sequence prediction tasks because they capture long-term dependencies in data. The model is trained on the text of Shakespeare's "Hamlet," providing a rich and complex dataset that challenges the model's predictive capabilities.

The project utilizes advanced deep-learning techniques to enhance natural language processing capabilities, specifically in next-word prediction tasks. By leveraging LSTM networks, it captures intricate patterns and dependencies within text data, ultimately providing a tool for applications such as text auto-completion and interactive chatbots. The deployment through a web application allows for user-friendly interaction with the model, showcasing its practical utility in real-time scenarios.

## Workflow Description

The project follows a structured workflow consisting of six key steps:

1- `Data Collection`: We use the text of Shakespeare's "Hamlet" as our dataset. This rich, complex text provides a good challenge for our model.

2- `Data Preprocessing`: The text data is tokenized, converted into sequences, and padded to ensure uniform input lengths. The sequences are then split into training and testing sets.

3- `Model Building`: An LSTM model is constructed with an embedding layer, two LSTM layers, and a dense output layer with a softmax activation function to predict the probability of the next word.

4- `Model Training`: The model is trained using the prepared sequences, with early stopping implemented to prevent overfitting. Early stopping monitors the validation loss and stops training when the loss stops improving.

5- `Model Evaluation`: The model is evaluated using a set of example sentences to test its ability to predict the next word accurately.

6- `Deployment`: A Streamlit web application is developed to allow users to input a sequence of words and get the predicted next word in real-time.

## Requirements💻 :

Ensure you have the following dependencies installed:

- Python (version 3.11.x || 3.12.x)

- IDE: VS-CODE or collab

- Virtual-environment(venv)

- Other dependencies (refer to the requirement.txt)

You can install the required Python packages using:

```bash

pip install -r requirement.txt

```

## Setup 💿:

- Clone the repository:

```bash

git clone https://github.com/SINGHxTUSHAR/NextWordAI.git

cd NextWordAI

```

- Create a virtual environment (optional but recommended):

```bash

python -m venv venv

```

- Activate the virtual environment:

  - On Windows:

   ```bash

   venv\Scripts\activate

   ```

  - On macOS/Linux:

  ```bash

  source venv/bin/activate

  ```

## Contributing 📌:

If you'd like to contribute to this project, please follow the standard GitHub fork and pull request process. Contributions, issues, and feature requests are welcome!

## Suggestion 🚀: 

If you have any suggestions for me related to this project, feel free to contact me at tusharsinghrawat.delhi@gmail.com or LinkedIn.

## License 📝:

This project is licensed under the MIT License - see the LICENSE file for details.